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Title
Remote sensing for resources development and environmental management
Author
Damen, M. C. J.

99
Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986
Spatial feature extraction from radar imagery
G.Bellavia
Computer Science, Aston University, Birmingham, UK
J.Elgy
Civil Engineering, Aston University, Birmingham, UK
ABSTRACT: It is accepted that the major role of remote sensing as an
information source will be in its contribution to geographical information
systems. With the advances in remote sensing, images are being created at an
increasing rate. The extraction of information from such data is traditionally
done manually and is thus costly in both time and money. Therefore techniques
need to be developed which automatically extract information from remotely
sensed images.
This paper considers the extraction of thin line features such as forest
rides, dykes and streams from active microwave imagery. Because radar images
are coherently created speckle is produced which renders traditional feature
extraction methods virtually useless. It is assumed that global techniques
such as generalised hough transforms or intelligent graph searching will be
more successful than simple local methods.
1 INTRODUCTION
This paper considers the extraction of thin
line features such as forest rides, dykes and
streams from remotely sensed active microwave
imagery. In the context of this paper remote
sensing is the aquisition of digital images
of the Earth from either airborne or
satellite sensor systems. Although digital
remote sensing started as late as 1972 many
satellite systems have been successfully
initiated with a rapidly increasing variety
of sensor types and specifications.
Several problems arise from this rapid
advance in remote sensing technology. The
efficient use of image data obtained from
satellites or aircraft relies on the
availability of human expertise and
sophisticated computer systems. In particular
radar imagery with its virtual continous
monitoring capability and high resolution has
shown itself to be superior to more
conventional imagery for a variety of
applications. But to fully utilise the
potential information in this form of data,
processing techniques which overcome the
inherent speckle noise need to be developed.
In general, the problem is one of storage
and processing of the data, which is not
currently met by computers or human effort.
Hardy(1985) states that the population of
Earth resource satellites will generate
something approaching 10 16 bits in 1986.
Assuming an average scene size of 4000x4000
pixels and 8 bits/pixel radiometric
resolution, a simple calculation shows this
data rate to be equivalent to about 200,000
single channel images/day. On common Computer
Compatible Tapes (CCT's) which can store
approximately 33 million bytes (MB), this
data would require about 10^ CCT's/day. This
rate signifies an increase of the data
volume/sensor/unit land area of more than an
order of magnitude over the last ten years
and results in large quantities of unused
data. To alleviate this waste of data there
is a need for the automatic interpretation of
remotely sensed images by computer.
It is accepted that the major role of remote
sensing as an information source will be in
its contribution to geographical information
systems. This role requires the extraction of
semantic information from remotely sensed
images and therefore automatic
interpretation.
The two arguments concerning data rates and
GIS bring us to the conclusion that automatic
interpretation is needed to fully realise the
potential of remote sensing.
The automatic interpretation process entails
several other processes. One of the first
steps is segmentation which aims to partition
the image into separate distinct regions.
Feature extraction techniques are also used
to enable the segmentation. The segmented
scene can then undergo a pattern recognition
process using world knowledge giving an
interpreted scene. Knowledge may also be used
at different stages in the interpretive
process. For example to assist or iteratively
refine feature extraction, (see fia. 1).
Real world images can be considered as
comprising of three major high level spatial
feature types.
Feature extraction Pattern
fig. 1 different schemes for the automatic
interpretation process.